Networking Genetic Evidence to Prioritise Drug Targets at the Gene, Pathway and Network Level

1 Overview

We are strong supporters of open-source tools for reproducible research. Priority index or Pi is developed as a genomic-led target prioritisation system, with the focus on leveraging genetic data to prioritise targets at the gene and pathway level. Based on evidence of genetic association from GWAS data, this prioritisation system is able to resolve modulated genes (seed genes) by utilising knowledge of linkage disequilibrium (co-inherited variants), distance from the gene, and evidence of genetic association with gene expression. Seed genes are scored in an integrative way quantifying the genetic influence. Scored seed genes are subsequently used as baits to rank seed genes plus additional (non-seed) genes; this is achieved by iteratively exploring the global connectivity of a gene interaction network. Genes with the top priority are further used to identify/prioritise pathways that are significantly enriched with highly prioritised genes. Prioritised genes are also used to identify a gene network interconnecting many highly prioritised genes but a few lowly prioritised genes as linkers.

In the Showcases section, we illustrate the power of Pi to perform disease-centric genetic target prioritisation at the gene, pathway and network level using several inflammatory diseases as exemplars, including Ankylosing Spondylitis, Spondyloarthritis, and Systemic Lupus Erythematosus.

In the Ontology annotations & TPN nominations section, we integrate ontology annotations and TPN nominations as an additional support for disease-specific genetic prioritisation by Pi.

In the Cross-disease comparisons section, we summarise and compare results of genetic target prioritisation across diseases, done at the gene, pathway and network level.

2 Web app

A web-based interface of Pi is available here, which allows on-the-fly prioritisation for a user-defined SNP list.

3 Workflow

4 R functions

This intends for end-users who are comfortable with R (with the latest version available at http://cran.r-project.org). Priority functions are all contained in a new package called PI which can be installed following instructions below:

# install the dependant packages including `XGR` and other packages used here
source("http://bioconductor.org/biocLite.R")
biocLite(c("XGR","devtools","VennDiagram"), siteRepos=c("http://cran.r-project.org"))

# also, install the `PI` package itself
library(devtools)
install_github(c("hfang-bristol/PI"), dependencies=T)

Priority functions are designed in a nested way. The core relation follows this route: xPrioritiserSNPs -> xPrioritiserGenes -> xPrioritiser -> xRWR, achieving gene-level prioritisation from an input list of SNPs (along with their significant level). The output of this route is taken as the input of either xPrioritiserManhattan for visualising gene priority scores, xPrioritiserPathways for prioritising pathways, or xPrioritiserSubnet for identifying a network of top prioritised genes.

4.1 xRWR

xRWR: implements Random Walk with Restart (RWR) estimating the affinity score of nodes in a graph to a list of seed nodes. The affinity score can be viewed as the influential impact over the graph that is collectively imposed by seed nodes. If the seeds are not given, it will pre-compute affinity matrix for nodes in the input graph with respect to each starting node (as a seed) by looping over every node in the graph. A higher-level function xPrioritiser directly relies on it.

4.2 xPrioritiser

xPrioritiser: uses RWR to calculate the affinity score of nodes in a graph to a list of seed nodes. A node that has a higher affinity score to seed nodes will receive a higher priority score. It is an internal function acting as a general template for RWR-based prioritisation. A higher-level function xPrioritiserGenes directly relies on it.

4.3 xPrioritiserGenes

xPrioritiserGenes: prioritises gene targets from an input gene interaction network and the score info imposed on its seed nodes/genes. This function can be considered to be a specific instance of xPrioritiser, that is, specifying a gene interaction network as a graph and seed nodes as seed genes.

There are two sources of gene interaction network information: the STRING database (Szklarczyk et al. 2015) and the Pathways Commons database (Cerami et al. 2011). STRING is a meta-integration of undirect interactions from a functional aspect, while Pathways Commons mainly contains both undirect and direct interactions from a physical/pathway aspect. In addition to interaction type, users can choose the interactions of varying quality:

Code	Interaction (type and quality)	Database
STRING_high	Functional interactions (with high confidence scores>=700)	STRING
STRING_medium	Functional interactions (with medium confidence scores>=400)	STRING
PCommonsUN_high	Physical/undirect interactions (with references & >=2 sources)	Pathways Commons
PCommonsUN_medium	Physical/undirect interactions (with references & >=1 sources)	Pathways Commons
PCommonsDN_high	Pathway/direct interactions (with references & >=2 sources)	Pathways Commons
PCommonsDN_medium	Pathway/direct interactions (with references & >=1 sources)	Pathways Commons

4.4 xPrioritiserSNPs

xPrioritiserSNPs: prioritises gene targets from an input gene interaction network and a given list of SNPs together with the significance level (eg GWAS reported p-values). To do so, it first defines seed genes and their scores that are calculated in an integrative manner to quantify the genetic influence under SNPs. Genetic influential score on a seed gene is calculated from the SNP score (reflecting the SNP significant level), the gene-to-SNP distance weight and the eQTL mapping weight. This function can be considered to be a specific instance of xPrioritiserGenes, that is, specifying seed genes plus their scores.

Knowledge of co-inherited variants is also used to include additional SNPs that are in Linkage Disequilibrium (LD) with GWAS lead SNPs. LD SNPs are calculated based on 1000 Genomes Project data (1000 Genomes Project Consortium 2012). LD SNPs are defined to be any SNPs having R2 > 0.8 with GWAS lead SNPs. The user can choose the population used to calculate LD SNPs:

Code	Population	Project
AFR	African	1000 Genomes Project
AMR	Admixed American	1000 Genomes Project
EAS	East Asian	1000 Genomes Project
EUR	European	1000 Genomes Project
SAS	South Asian	1000 Genomes Project

4.5 xPrioritiserManhattan

xPrioritiserManhattan: visualises prioritised genes using manhattan plot, in which priority for genes is displayed on the Y-axis along with genomic locations on the X-axis. Also highlighted are genes with the top priority.

4.6 xPrioritiserPathways

xPrioritiserPathways: prioritises pathways based on enrichment analysis of genes with the top priority (eg top 100 genes) using a compendium of pathways. A highly prioritised pathway has its member genes with high gene-level priority scores, that is, having evidence of direct modulation by disease-associated lead (or LD) SNPs, and/or having evidence of indirect modulation at the network level.

In addition to pathways, enrichment analysis can be extended to other ontologies, as listed below:

Code	Ontology	Category
DO	Disease Ontology	Disease
GOMF	Gene Ontology Molecular Function	Function
GOBP	Gene Ontology Biological Process	Function
GOCC	Gene Ontology Cellular Component	Function
HPPA	Human Phenotype Phenotypic Abnormality	Phenotype
HPMI	Human Phenotype Mode of Inheritance	Phenotype
HPCM	Human Phenotype Clinical Modifier	Phenotype
HPMA	Human Phenotype Mortality Aging	Phenotype
MP	Mammalian/Mouse Phenotype	Phenotype
DGIdb	DGI druggable gene categories	Druggable
SF	SCOP domain superfamilies	Domain
PS2	phylostratific age information (our ancestors)	Evolution
MsigdbH	Hallmark gene sets	Hallmark (MsigDB)
MsigdbC1	Chromosome and cytogenetic band positional gene sets	Cytogenetics (MsigDB)
MsigdbC2CGP	Chemical and genetic perturbation gene sets	Perturbation (MsigDB)
MsigdbC2CPall	All pathway gene sets	Pathways (MsigDB)
MsigdbC2CP	Canonical pathway gene sets	Pathways (MsigDB)
MsigdbC2KEGG	KEGG pathway gene sets	Pathways (MsigDB)
MsigdbC2REACTOME	Reactome pathway gene sets	Pathways (MsigDB)
MsigdbC2BIOCARTA	BioCarta pathway gene sets	Pathways (MsigDB)
MsigdbC3TFT	Transcription factor target gene sets	TF targets (MsigDB)
MsigdbC3MIR	microRNA target gene sets	microRNA targets (MsigDB)
MsigdbC4CGN	Cancer gene neighborhood gene sets	Cancer (MsigDB)
MsigdbC4CM	Cancer module gene sets	Cancer (MsigDB)
MsigdbC5BP	GO biological process gene sets	Function (MsigDB)
MsigdbC5MF	GO molecular function gene sets	Function (MsigDB)
MsigdbC5CC	GO cellular component gene sets	Function (MsigDB)
MsigdbC6	Oncogenic signature gene sets	Oncology (MsigDB)
MsigdbC7	Immunologic signature gene sets	Immunology (MsigDB)

4.7 xPrioritiserSubnet

xPrioritiserSubnet: identifies a gene network that contains as many highly prioritised genes as possible but a few lowly prioritised genes as linkers. An iterative procedure of choosing different priority cutoff is also used to identify the network with a desired number of nodes/genes.

5 Showcases

In this section, we give a step-by-step demo of using Pi to carry out disease-specific genetic prioritisation of targets at the gene and pathway level, focusing on three inflammatory diseases as exemplars: Ankylosing Spondylitis (AS), Spondyloarthritis (SA, including AS and Psoriatic Arthritis), and Systemic Lupus Erythematosus (SLE).

First of all, load the packages including PI:

library(PI)
# the following packages are also needed
library(XGR)
library(GenomicRanges)
library(ggplot2)
library(RCircos)
library(VennDiagram)

# optionally, specify the local location of built-in RData
# by default:
RData.location <- "https://github.com/hfang-bristol/RDataCentre/blob/master/XGR/1.0.0"

5.1 Ankylosing Spondylitis

GWAS SNPs are collected mainly from GWAS Catalog (Welter et al. 2014), complemented by ImmunoBase and latest publications.

Import AS-associated GWAS lead SNPs (with the help of Anna Sanniti)

AS <- read.delim(file.path(path.package("PI"),"AS.txt"), stringsAsFactors=F)

The first 5 rows of the data frame AS are shown below, with the column SNP for AS GWAS SNPs and the column Pval for GWAS-detected P-values.

SNP	Pval
rs10045403	5.8e-14
rs1018326	2.0e-06
rs10440635	3.0e-07
rs10781500	1.0e-06
rs10865331	1.9e-19

5.1.1 Gene-level prioritisation

It includes the following steps:

1. Define seed genes based on distance-to-SNP window and genetic association with gene expression: nearby genes that are located within 200kb distance window of lead or Linkage Disequilibrium (LD) SNPs that are calculated based on European population data from 1000 Genome Project; expression associated genes (eQTL genes) for which gene expression is, either in a cis- or trans-acting manner, significantly associated with lead or LD SNPs, based on immune-stimulated eQTL data in monocytes (Fairfax et al. 2014).

2. Score seed genes to quantify the genetic influence under lead or LD SNPs.

3. Prioritise target genes by estimating their global network connectivity to seed genes. With scored seed genes superposed onto a gene interaction network (see above STRING_high), RWR algorithm is implemented to prioritise candidate target genes based on their network connectivity/affinity to seed genes. As such, a gene that has a higher affinity score to seed genes will receive a higher priority score.

pAS <- xPrioritiserSNPs(data=AS, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, restart=0.75, RData.location=RData.location)

The results are stored in the data frame pAS$priority, which can be saved into a file AS.genes_priority.txt:

AS_genes <- pAS$priority
write.table(AS_genes, file="AS.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
CAST	1	70.94986	0.05574	1
ERAP2	1	42.47168	0.03338	2
ERAP1	1	40.05554	0.03171	3
B3GNT2	1	23.33775	0.01838	4
LNPEP	1	22.31999	0.01767	5
IL23R	1	22.39796	0.01765	6
DDX39B	1	16.84224	0.01323	7
TBKBP1	1	15.61992	0.01230	8
NFKBIL1	1	13.79161	0.01154	9
ETS2	1	14.62834	0.01151	10
IL12RB2	1	14.34654	0.01136	11
KIF21B	1	11.32791	0.00948	12
PSMG1	1	11.96717	0.00945	13
C1orf106	1	10.42024	0.00827	14
IL6R	1	9.83556	0.00778	15
TNFRSF1A	1	8.42002	0.00690	16
GPR65	1	8.70283	0.00683	17
NPEPPS	1	8.24430	0.00648	18
KPNB1	1	8.19357	0.00645	19
NKX2-3	1	7.90464	0.00622	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_AS <- xPrioritiserManhattan(pAS, highlight.top=20, cex=0.5, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_AS)

5.1.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pAS, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `AS.pathways_priority.txt`
AS_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(AS_pathways), AS_pathways)
write.table(output, file="AS.pathways_priority.txt", sep="\t", row.names=F)

The top 5 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
IL12-mediated signaling events	63	8	11.30	4.9e-10	2.3e-08	NFKB1, IL12B, TYK2, IL12RB2, IL1R1, IL2RA, NOS2, TBX21
IL27-mediated signaling events	26	5	11.20	2.2e-08	5.1e-07	IL12B, TYK2, IL12RB2, TBX21, IL27
NO2-dependent IL 12 Pathway in NK cells	17	4	11.10	9.4e-08	1.4e-06	IL12B, TYK2, IL12RB2, NOS2
IL23-mediated signaling events	37	5	9.23	2.1e-07	2.4e-06	NFKB1, IL12B, TYK2, NOS2, IL23R
Cytokine-cytokine receptor interaction	266	11	6.70	3.2e-07	2.9e-06	CSF2RB, TNFRSF1A, IL12B, IL6R, IL12RB2, IL1R1, IL2RA, LTBR, IL23R, IL1R2, CD27

This barplot displays the top 5 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- AS_pathways[1:5,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

• SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pAS, priority.top=100, ontology="SF", RData.location=RData.location)
AS_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 8 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Leukotriene A4 hydrolase N-terminal domain	12	4	15.60	2.9e-09	3.2e-08	LNPEP, ERAP2, NPEPPS, ERAP1
TNF receptor-like	24	3	8.06	7.3e-06	4.0e-05	TNFRSF1A, CD27, LTBR
Immunoglobulin	481	10	4.74	4.3e-05	1.6e-04	TAPBPL, IL1R2, ICAM3, ICAM4, FCGR2A, FCGR2B, FCGR3A, IL1R1, IL6R, IL12B
p53-like transcription factors	43	3	5.81	7.8e-05	2.1e-04	NFKB1, RUNX3, TBX21
Metalloproteases (zincins), catalytic domain	100	4	4.78	1.9e-04	4.2e-04	LNPEP, ERAP2, NPEPPS, ERAP1
Histone-fold	107	4	4.57	2.6e-04	4.8e-04	HIST1H4I, HIST1H3A, HIST1H3C, HIST1H2BD
Fibronectin type III	200	5	3.85	6.8e-04	1.1e-03	IL6R, IL12B, IL12RB2, CSF2RB, IL23R
DEATH domain	87	3	3.75	1.2e-03	1.6e-03	NFKB1, CARD9, TNFRSF1A

• GO molecular function

eTerm <- xPrioritiserPathways(pNode=pAS, priority.top=100, ontology="GOMF", RData.location=RData.location)
AS_gomf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Leukotriene A4 hydrolase N-terminal domain	12	4	15.600	2.9e-09	3.2e-08	LNPEP, ERAP2, NPEPPS, ERAP1
TNF receptor-like	24	3	8.060	7.3e-06	4.0e-05	TNFRSF1A, CD27, LTBR
Immunoglobulin	481	10	4.740	4.3e-05	1.6e-04	TAPBPL, IL1R2, ICAM3, ICAM4, FCGR2A, FCGR2B, FCGR3A, IL1R1, IL6R, IL12B
p53-like transcription factors	43	3	5.810	7.8e-05	2.1e-04	NFKB1, RUNX3, TBX21
Metalloproteases (zincins), catalytic domain	100	4	4.780	1.9e-04	4.2e-04	LNPEP, ERAP2, NPEPPS, ERAP1
Histone-fold	107	4	4.570	2.6e-04	4.8e-04	HIST1H4I, HIST1H3A, HIST1H3C, HIST1H2BD
Fibronectin type III	200	5	3.850	6.8e-04	1.1e-03	IL6R, IL12B, IL12RB2, CSF2RB, IL23R
DEATH domain	87	3	3.750	1.2e-03	1.6e-03	NFKB1, CARD9, TNFRSF1A
Homeodomain-like	294	3	1.160	7.2e-02	8.8e-02	NKX2-3, MIER1, SNAPC4
PH domain-like	420	3	0.518	1.9e-01	2.0e-01	TYK2, PLEKHG6, SH2B3

5.1.3 Network-level prioritisation

Network-level prioritisation is to identify a gene network that contains as many highly prioritised genes as possible but a few lowly prioritised genes as linkers. Given a gene network (the same as used in gene-level prioritisation) with nodes labelled with gene priority scores, the search for a maximum-scoring gene subnetwork is briefed as follows:

1. score transformation, that is, given a threshold of tolerable priority scores, nodes above this threshold (nodes of interest) are scored positively, and negative scores for nodes below the threshold (intolerable).

2. subnetwork identification, that is, to find an interconnected gene subnetwork enriched with positive-score nodes but allowing for a few negative-score nodes as linkers, done via heuristically solving prize-collecting Steiner tree problem (Fang and Gough 2014).

3. controlling the subnetwork size, that is, an iterative procedure of scanning different priority thresholds is used to identify the network with a desired number of nodes/genes.

Notably, the preferential use of the same network as used in gene-level prioritisation is due to the fact that gene-level affinity/priority scores are smoothly distributed over the network after being walked. In other words, the chance of identifying such a gene network enriched with top prioritised genes is much higher. To reduce the runtime, by default only top 10% prioritised genes will be used to search for the maximum-scoring gene network.

# find maximum-scoring gene network with the desired node number=50
AS_net <- xPrioritiserSubnet(pNode=pAS, priority.quantite=0.1, subnet.size=50, RData.location=RData.location)

The identified gene network has nodes/genes colored according to their priority scores (see below). Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.

g <- AS_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

• Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
IL12-mediated signaling events	63	11	18.90	1.5e-16	9.0e-15	NFKB1, B2M, IL12B, SOCS1, TYK2, IL12RB2, IL1R1, IL2RA, NOS2, SPHK2, TBX21
IL27-mediated signaling events	26	5	13.40	2.8e-09	8.3e-08	IL12B, TYK2, IL12RB2, TBX21, IL27
Cytokine-cytokine receptor interaction	266	11	8.40	5.6e-09	1.1e-07	CSF2RB, TNFRSF1A, IL12B, IL6R, IL12RB2, IL1R1, IL2RA, CXCR1, CXCR2, IL23R, IL1R2
NO2-dependent IL 12 Pathway in NK cells	17	4	13.30	1.7e-08	2.5e-07	IL12B, TYK2, IL12RB2, NOS2
IL23-mediated signaling events	37	5	11.10	2.7e-08	3.2e-07	NFKB1, IL12B, TYK2, NOS2, IL23R
Jak-STAT signaling pathway	155	8	8.17	6.9e-08	6.1e-07	CSF2RB, IL12B, SOCS1, IL6R, TYK2, IL12RB2, IL2RA, IL23R
Cytokine Signaling in Immune system	268	10	7.49	7.1e-08	6.1e-07	CSF2RB, ICAM1, B2M, SOCS1, IL6R, TYK2, IL1R1, IL2RA, IL1R2, UBA52
Hematopoietic cell lineage	87	6	8.37	2.5e-07	1.9e-06	ITGB3, IL6R, IL1R1, IL2RA, IL1R2, CD9
Signaling by Interleukins	106	6	7.46	9.6e-07	6.4e-06	CSF2RB, IL6R, TYK2, IL1R1, IL2RA, IL1R2
Immune System	912	16	5.54	1.1e-06	6.6e-06	CSF2RB, ICAM1, B2M, SOCS1, IL6R, TYK2, IL1R1, IL2RA, ICAM3, CDC27, IL1R2, LNPEP, UBA52, ICAM4, ERAP1, NPEPPS

• Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 5 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Neutral zinc metallopeptidase	182	4	3.76	0.00084	0.0023	ERAP2, LNPEP, NPEPPS, ERAP1
Cell surface	467	7	3.74	0.00047	0.0023	TNFRSF1A, B2M, SCNN1A, ICAM1, CD9, IL1R1, CXCR2
External side of plasma membrane	189	4	3.66	0.00099	0.0023	B2M, SCNN1A, ICAM1, CD9
Drug resistance	349	4	2.15	0.01400	0.0240	B2M, ICAM1, SOCS1, PDE4A
Tumor suppressor	716	6	1.83	0.02500	0.0350	UBA52, IL12B, CDC27, RHOA, CDC37, RUNX3

5.2 Spondyloarthritis

Import SA-associated GWAS lead SNPs (with the help of Anna Sanniti and Alicia Lledo Lara)

SA <- read.delim(file.path(path.package("PI"),"Spondyloarthritis.txt"), stringsAsFactors=F)

5.2.1 Gene-level prioritisation

pSA <- xPrioritiserSNPs(data=SA, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, restart=0.75, RData.location=RData.location)

The results are stored in the data frame pSA$priority, which can be saved into a file SA.genes_priority.txt:

SA_genes <- pSA$priority
write.table(SA_genes, file="SA.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
DDX39B	1	91.60697	0.02378	1
NFKBIL1	1	75.01421	0.02074	2
CAST	1	70.94986	0.01842	3
ATF6B	1	62.40788	0.01621	4
TRAF3IP2	1	50.87129	0.01321	5
ERAP2	1	42.47168	0.01103	6
ERAP1	1	40.05554	0.01048	7
TNIP1	1	37.35657	0.00978	8
ANXA6	1	35.85679	0.00932	9
LCE3D	1	28.05582	0.00912	10
IER3	1	34.61371	0.00898	11
LCE3A	1	27.80789	0.00858	12
LCE3B	1	27.93689	0.00830	13
LCE3C	1	24.05016	0.00800	14
TYK2	1	28.70260	0.00756	15
LCE3E	1	24.55941	0.00749	16
MDC1	1	28.35607	0.00737	17
VARS2	1	25.92044	0.00673	18
ICAM3	1	24.84533	0.00662	19
B3GNT2	1	23.33775	0.00607	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_SA <- xPrioritiserManhattan(pSA, highlight.top=20, cex=0.4, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_SA)

5.2.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pSA, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `SA.pathways_priority.txt`
SA_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(SA_pathways), SA_pathways)
write.table(output, file="SA.pathways_priority.txt", sep="\t", row.names=F)

The top 10 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
IL27-mediated signaling events	26	4	9.64	4.4e-07	7.9e-06	STAT2, IL12B, TYK2, IL12RB2
Jak-STAT signaling pathway	155	8	7.30	3.6e-07	7.9e-06	STAT2, IL12B, IL6R, TYK2, IL12RB2, CTF1, IL23A, IL23R
Beta2 integrin cell surface interactions	29	4	9.09	7.8e-07	9.3e-06	ICAM1, ITGAM, ICAM3, ICAM4
NO2-dependent IL 12 Pathway in NK cells	17	3	8.98	2.9e-06	2.1e-05	IL12B, TYK2, IL12RB2
IL23-mediated signaling events	37	4	7.94	2.7e-06	2.1e-05	IL12B, TYK2, IL23A, IL23R
IL 6 signaling pathway	22	3	7.82	8.7e-06	5.2e-05	CSNK2A1, IL6R, TYK2
IL12 and Stat4 Dependent Signaling Pathway in Th1 Development	23	3	7.63	1.0e-05	5.4e-05	IL12B, TYK2, IL12RB2
Allograft rejection	37	3	5.84	7.3e-05	2.9e-04	IL12B, HLA-E, HLA-DQA1
Viral myocarditis	71	4	5.43	7.0e-05	2.9e-04	FYN, ICAM1, HLA-E, HLA-DQA1
amb2 Integrin signaling	41	3	5.50	1.1e-04	3.8e-04	FYN, ICAM1, ITGAM

This barplot displays the top 10 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- SA_pathways[1:10,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

• SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pSA, priority.top=100, ontology="SF", RData.location=RData.location)
SA_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 6 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Leukotriene A4 hydrolase N-terminal domain	12	3	12.30	2.3e-07	2.1e-06	LNPEP, ERAP2, ERAP1
Immunoglobulin	481	8	3.83	4.4e-04	2.0e-03	HLA-DQA1, HLA-E, ICAM1, ICAM3, ICAM4, ICAM5, IL6R, IL12B
Metalloproteases (zincins), catalytic domain	100	3	3.66	1.3e-03	4.0e-03	LNPEP, ERAP2, ERAP1
SH2 domain	112	3	3.38	2.0e-03	4.5e-03	TYK2, STAT2, FYN
Fibronectin type III	200	4	3.14	2.7e-03	4.8e-03	IL6R, IL12B, IL12RB2, IL23R
RNA-binding domain, RBD	251	3	1.66	3.2e-02	4.8e-02	RAVER1, SETD1A, NELFE

• GO molecular function

eTerm <- xPrioritiserPathways(pNode=pSA, priority.top=100, ontology="GOMF", RData.location=RData.location)
SA_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
aminopeptidase activity	28	3	7.50	1.3e-05	0.00025	ERAP2, LNPEP, ERAP1
integrin binding	105	4	4.70	2.2e-04	0.00210	ICAM1, ICAM3, ICAM4, ICAM5
ATP-dependent RNA helicase activity	63	3	4.69	3.2e-04	0.00210	DHX30, SKIV2L, DDX39B
RNA binding	404	6	2.73	5.0e-03	0.02500	RPL34, SETD1A, NELFE, ILF3, SKIV2L, PUS10
cytokine activity	172	3	2.24	1.2e-02	0.04900	IL12B, IL23A, CTF1
nucleotide binding	304	4	1.95	2.1e-02	0.07000	RAVER1, REV3L, SETD1A, NELFE
poly(A) RNA binding	1118	10	1.81	2.9e-02	0.07700	MRPL4, RAVER1, NELFE, DHX30, SSRP1, XRCC6, ILF3, CAST, DDX39B, CS
signal transducer activity	227	3	1.69	3.1e-02	0.07700	STAT2, SLC44A2, KCNH7
enzyme binding	309	3	1.11	7.7e-02	0.16000	KAT8, IL6R, DNM2
receptor binding	321	3	1.04	8.6e-02	0.16000	HLA-E, ICAM3, APOF

5.2.3 Network-level prioritisation

# find maximum-scoring gene network with the desired node number=50
SA_net <- xPrioritiserSubnet(pNode=pSA, priority.quantite=0.1, subnet.size=50, RData.location=RData.location)

The identified gene network has nodes/genes colored according to their priority scores (see below). Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.

g <- SA_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

• Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Beta2 integrin cell surface interactions	29	7	17.60	1.2e-12	1.0e-10	ICAM1, ITGAL, ITGAX, ITGAM, ICAM3, FCGR2A, ICAM4
Immune System	912	22	8.34	1.5e-11	6.4e-10	NFKBIA, ITGB1, KEAP1, RPS6KA1, STAT2, FYN, ICAM1, ITGAL, TNFAIP3, IL6R, TYK2, IL1R1, PSMA6, ICAM3, RNF41, LNPEP, UBA52, REL, SQSTM1, ICAM4, ERAP1, NPEPPS
Integrin cell surface interactions	79	8	11.90	2.1e-10	5.9e-09	ITGB1, ICAM1, ITGAL, ITGAX, ITGB3, ITGAM, ICAM3, ICAM4
Jak-STAT signaling pathway	155	10	10.30	2.9e-10	6.2e-09	IL4, STAT2, IL12B, IL13, IL6R, TYK2, IL12RB2, CTF1, IL23A, IL23R
IL23-mediated signaling events	37	6	13.20	6.2e-10	1.1e-08	NFKBIA, IL12B, TYK2, NOS2, IL23A, IL23R
IL12-mediated signaling events	63	7	11.70	9.4e-10	1.4e-08	IL4, IL12B, TYK2, IL12RB2, IL1R1, NOS2, TBX21
Monocyte and its Surface Molecules	11	4	16.50	1.4e-09	1.8e-08	ITGB1, ICAM1, ITGAL, ITGAM
IL27-mediated signaling events	26	5	13.20	3.2e-09	2.7e-08	STAT2, IL12B, TYK2, IL12RB2, TBX21
Integrin family cell surface interactions	26	5	13.20	3.2e-09	2.7e-08	ITGB1, ITGAL, ITGAX, ITGB3, ITGAM
Leishmania infection	72	7	10.80	2.8e-09	2.7e-08	NFKBIA, IL4, ITGB1, IL12B, ITGAM, NOS2, FCGR2A

• Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 5 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
External side of plasma membrane	189	7	6.60	1.6e-06	1.6e-05	SCNN1A, ITGAL, ITGAX, ITGB1, ICAM1, IL4, IL13
Cell surface	467	10	5.50	4.1e-06	2.0e-05	TNFRSF1A, SCNN1A, STX4, ITGAL, ITGAX, ITGB1, ICAM1, IL1R1, IL4, IL13
Tumor suppressor	716	8	2.70	4.2e-03	1.4e-02	ITGB1, TNFAIP3, UBA52, IL12B, CSNK2A1, PSMA6, CDC37, RUNX3
Protease inhibitor	179	3	2.40	9.2e-03	1.9e-02	TNFAIP3, RPS6KA1, CAST
Neutral zinc metallopeptidase	182	3	2.36	9.7e-03	1.9e-02	LNPEP, NPEPPS, ERAP1

5.3 Systemic Lupus Erythematosus

Import SLE-associated GWAS lead SNPs

SLE <- read.delim(file.path(path.package("PI"),"SLE.txt"), stringsAsFactors=F)

5.3.1 Gene-level prioritisation

pSLE <- xPrioritiserSNPs(data=SLE, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, restart=0.75, RData.location=RData.location)

The results are stored in the data frame pSLE$priority, which can be saved into a file SLE.genes_priority.txt:

SLE_genes <- pSLE$priority
write.table(SLE_genes, file="SLE.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
ATF6B	1	219.97756	0.03584	1
NELFE	1	158.67378	0.02596	2
VARS2	1	132.00642	0.02149	3
ITGAM	1	128.93662	0.02123	4
PBX2	1	119.14179	0.01948	5
IRF5	1	118.38794	0.01938	6
STAT4	1	117.28679	0.01930	7
TNPO3	1	104.72725	0.01708	8
CUEDC2	1	99.35864	0.01616	9
HLA-DPB1	1	88.83311	0.01481	10
RNF114	1	80.66186	0.01319	11
CLIC1	1	79.67787	0.01301	12
PSMB9	1	78.94596	0.01287	13
C2	1	74.40265	0.01216	14
NFKBIL1	1	69.84825	0.01211	15
PYCARD	1	72.72229	0.01192	16
LTA	1	62.00410	0.01068	17
SKIV2L	1	62.27032	0.01046	18
HNRNPM	1	60.48517	0.00989	19
ITGAX	1	56.59653	0.00955	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_SLE <- xPrioritiserManhattan(pSLE, highlight.top=20, cex=0.4, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_SLE)

5.3.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pSLE, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `SLE.pathways_priority.txt`
SLE_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(SLE_pathways), SLE_pathways)
write.table(output, file="SLE.pathways_priority.txt", sep="\t", row.names=F)

The top 10 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Beta2 integrin cell surface interactions	29	5	10.60	4.1e-08	2.2e-06	ITGAX, ITGAM, ICAM3, FCGR2A, ITGAD
Type I diabetes mellitus	43	5	8.57	4.8e-07	1.2e-05	CD80, LTA, HLA-DOB, HLA-DPB1, HLA-DQA1
Leishmania infection	72	6	7.76	6.4e-07	1.2e-05	STAT1, ITGAM, HLA-DOB, HLA-DPB1, HLA-DQA1, FCGR2A
Initial triggering of complement	13	3	9.63	1.5e-06	1.7e-05	C2, C4A, CFB
Regulation of Complement cascade	13	3	9.63	1.5e-06	1.7e-05	C2, C4A, CFB
IL22 Soluble Receptor Signaling Pathway	16	3	8.62	3.9e-06	3.4e-05	STAT1, STAT4, TYK2
Allograft rejection	37	4	7.35	5.5e-06	3.4e-05	CD80, HLA-DOB, HLA-DPB1, HLA-DQA1
Interferon gamma signaling	62	5	6.94	4.4e-06	3.4e-05	STAT1, HLA-DPB1, HLA-DQA1, IRF5, IRF8
Systemic lupus erythematosus	139	7	6.14	5.3e-06	3.4e-05	CD80, C2, C4A, HLA-DOB, HLA-DPB1, HLA-DQA1, FCGR2A
Complement Pathway	19	3	7.85	8.1e-06	4.4e-05	C2, C4A, CFB

This barplot displays the top 10 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- SLE_pathways[1:10,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

• SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pSLE, priority.top=100, ontology="SF", RData.location=RData.location)
SLE_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 8 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
vWA-like	95	6	7.11	1.9e-06	2.5e-05	CFB, C2, ITGAD, ITGAM, ITGAX, XRCC6
Integrin alpha N-terminal domain	23	3	7.61	1.1e-05	6.7e-05	ITGAD, ITGAM, ITGAX
Integrin domains	25	3	7.27	1.6e-05	6.7e-05	ITGAD, ITGAM, ITGAX
MHC antigen-recognition domain	40	3	5.56	1.0e-04	3.4e-04	HLA-DOB, HLA-DPB1, HLA-DQA1
TNF-like	53	3	4.69	3.2e-04	8.2e-04	LTB, TNFSF4, LTA
SH2 domain	112	4	4.00	6.4e-04	1.4e-03	TYK2, STAT1, STAT4, BLK
Trypsin-like serine proteases	120	3	2.64	6.5e-03	1.2e-02	CFB, PRSS8, C2
Immunoglobulin	481	6	1.79	2.9e-02	4.7e-02	HLA-DOB, HLA-DPB1, HLA-DQA1, ICAM3, CD80, FCGR2A

• GO molecular function

eTerm <- xPrioritiserPathways(pNode=pSLE, priority.top=100, ontology="GOMF", RData.location=RData.location)
SLE_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
tumor necrosis factor receptor binding	30	4	9.30	6.7e-07	1.9e-05	STAT1, LTA, LTB, TNFSF4
ATP-dependent RNA helicase activity	63	3	4.43	4.5e-04	6.3e-03	DHX30, SKIV2L, DDX39B
receptor binding	321	6	3.13	2.3e-03	1.6e-02	HSPA1A, ICAM3, LTA, BLK, LTB, TNFSF4
sequence-specific DNA binding transcription factor activity	825	11	3.00	2.4e-03	1.6e-02	ZNF668, IKZF1, PTTG1, MECP2, ETS1, ATF6B, PBX2, PHTF1, STAT1, STAT4, IRF5
actin filament binding	113	3	2.96	3.9e-03	1.9e-02	AIF1, MYO1B, FLNC
transcription factor binding	266	5	2.87	4.1e-03	1.9e-02	MECP2, ETS1, PBX2, GPX3, KAT8
enzyme binding	309	5	2.48	8.3e-03	3.3e-02	STAT1, UBE2L3, TSPAN33, PYCARD, KAT8
serine-type endopeptidase activity	154	3	2.29	1.2e-02	4.0e-02	PRSS8, C2, CFB
cytokine activity	172	3	2.06	1.7e-02	5.2e-02	LTA, LTB, TNFSF4
ligase activity	283	4	1.91	2.3e-02	6.1e-02	RNF114, UBE2L3, PPIL2, TNFAIP3

5.3.3 Network-level prioritisation

# find maximum-scoring gene network with the desired node number=50
SLE_net <- xPrioritiserSubnet(pNode=pSLE, priority.quantite=0.1, subnet.size=50, RData.location=RData.location)

The identified gene network has nodes/genes colored according to their priority scores (see below). Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.

g <- SLE_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

• Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Immune System	912	24	9.46	8.1e-14	9.1e-12	CD80, HRAS, STAT1, ICAM1, FCGR2B, CSK, CDKN1B, SOCS1, IRAK1, TYK2, CD44, SKP1, RASGRP3, ICAM3, IRF7, PYCARD, FCGR3A, RASGRP1, UBA52, IRF5, BLK, IRF8, SQSTM1, ICAM4
Beta2 integrin cell surface interactions	29	7	17.80	9.9e-13	5.6e-11	ICAM1, ITGAX, ITGAM, ICAM3, FCGR2A, ITGAD, ICAM4
Cytokine Signaling in Immune system	268	13	10.10	3.2e-11	1.2e-09	HRAS, STAT1, ICAM1, SOCS1, IRAK1, TYK2, CD44, SKP1, IRF7, UBA52, IRF5, IRF8, SQSTM1
Interferon gamma signaling	62	7	11.90	6.9e-10	1.9e-08	STAT1, ICAM1, SOCS1, CD44, IRF7, IRF5, IRF8
Leishmania infection	72	7	11.00	2.3e-09	5.1e-08	IL10, STAT1, TNF, ITGAM, IRAK1, FCGR2A, FCGR3A
Adaptive Immune System	524	15	7.76	2.7e-09	5.1e-08	CD80, HRAS, ICAM1, FCGR2B, CSK, CDKN1B, SOCS1, SKP1, RASGRP3, ICAM3, FCGR3A, RASGRP1, UBA52, BLK, ICAM4
Integrin cell surface interactions	79	7	10.40	5.0e-09	7.1e-08	ICAM1, CSK, ITGAX, ITGAM, ICAM3, RASGRP1, ICAM4
Interferon Signaling	158	9	9.20	5.0e-09	7.1e-08	STAT1, ICAM1, SOCS1, TYK2, CD44, IRF7, UBA52, IRF5, IRF8
IL-10 Anti-inflammatory Signaling Pathway	17	4	13.30	1.7e-08	2.1e-07	IL10, STAT1, STAT4, TNF
Interferon alpha/beta signaling	64	6	9.96	2.9e-08	3.2e-07	STAT1, SOCS1, TYK2, IRF7, IRF5, IRF8

• Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 5 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
External side of plasma membrane	189	6	5.06	4.9e-05	0.00059	TNF, ITGAX, ICAM1, CD80, FCGR3A, CD44
Clinically actionable	237	6	4.30	2.0e-04	0.00110	CDKN1B, SOCS1, TP53, HRAS, PRDM1, STAT4
Histone modification	249	6	4.14	2.8e-04	0.00110	TP53, CDK9, HCFC1, KAT8, MECP2, SKP1
Drug resistance	349	6	3.13	2.1e-03	0.00620	TNF, ICAM1, SOCS1, TP53, IL10, GPX3
Tumor suppressor	716	9	2.82	3.2e-03	0.00760	TNF, CDKN1B, TP53, HRAS, CDK9, UBA52, HCFC1, ETS1, CDC37

5.4 Dupuytren’s disease (DD)

Import DD-associated GWAS lead SNPs

DD <- read.delim("~/Sites/XGR/Dupuytren.SNPs.txt", stringsAsFactors=F)

The 13 DD-associated SNPs are:

SNPs	Pvalue
rs16879765	6e-39
rs6519955	3e-33
rs611744	8e-15
rs11672517	7e-14
rs2912522	2e-13
rs8124695	8e-10
rs7524102	3e-09
rs10809650	6e-09
rs4730775	3e-08
rs2179367	3e-07
rs4789939	6e-07
rs4932194	8e-07
rs12032381	6e-06

5.4.1 Gene-level prioritisation

pDD <- xPrioritiserSNPs(data=DD, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, restart=0.75, RData.location=RData.location)

The results are stored in the data frame pDD$priority, which can be saved into a file DD.genes_priority.txt:

DD_genes <- pDD$priority
write.table(DD_genes, file="DD.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
PRR34	1	23.71986	0.10881	1
SFRP4	1	24.36136	0.10538	2
NME8	1	20.14126	0.08687	3
WNT7B	1	17.54777	0.07620	4
EIF3E	1	9.79590	0.04216	5
DUXA	1	8.85389	0.03895	6
RSPO2	1	9.01925	0.03893	7
ZIM3	1	6.90278	0.03296	8
PPARA	1	7.48707	0.03227	9
USP29	1	5.92433	0.02925	10
BPIFC	0	0.00000	0.02720	11
AURKC	1	4.23335	0.01824	12
ATXN10	1	3.84695	0.01663	13
WNT2	1	3.22187	0.01475	14
STARD3NL	1	3.31155	0.01436	15
TIMP2	1	3.05084	0.01316	16
ZNF460	1	2.39640	0.01033	17
ZC3H12D	1	2.06816	0.00890	18
ZBTB40	1	1.91320	0.00837	19
NUP43	1	1.82247	0.00785	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_DD <- xPrioritiserManhattan(pDD, highlight.top=20, cex=0.4, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_DD)

5.4.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pDD, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `DD.pathways_priority.txt`
DD_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(DD_pathways), DD_pathways)
write.table(output, file="DD.pathways_priority.txt", sep="\t", row.names=F)

The top 10 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Basal cell carcinoma	55	27	43.0	0.0e+00	0.0e+00	FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B
Melanogenesis	101	27	31.4	0.0e+00	0.0e+00	FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B
Wnt signaling pathway	151	30	28.3	0.0e+00	0.0e+00	FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B, SFRP4, DKK2, DKK1
Class B/2 (Secretin family receptors)	87	25	31.4	0.0e+00	0.0e+00	FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, WNT3A, FZD10, WNT16, FZD5, FZD3
Genes related to Wnt-mediated signal transduction	89	25	31.0	0.0e+00	0.0e+00	FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT11, WNT2B, FZD4, WNT9A, FZD6, FZD7, FZD2, FZD8, WNT10A, WNT3A, WNT16, FZD5, FZD3, WNT5B, SFRP4, DKK1
Wnt signaling network	28	15	33.4	0.0e+00	0.0e+00	FZD1, WNT2, WNT3, WNT5A, WNT7A, WNT7B, FZD4, FZD6, FZD7, FZD2, FZD8, WNT3A, FZD10, FZD5, DKK1
Hedgehog signaling pathway	56	18	28.2	0.0e+00	0.0e+00	WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, WNT9A, WNT9B, WNT10A, WNT3A, WNT16, WNT5B
Pathways in cancer	325	27	16.7	0.0e+00	0.0e+00	FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B
GPCR ligand binding	400	25	13.5	2.8e-19	6.3e-19	FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, WNT3A, FZD10, WNT16, FZD5, FZD3
Genes encoding secreted soluble factors	344	20	11.5	6.8e-15	1.4e-14	WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, WNT9A, WNT9B, WNT10A, WNT3A, WNT16, WNT5B, SFRP4, MEGF6

This barplot displays the top 10 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- DD_pathways[1:10,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

• SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pDD, priority.top=100, ontology="SF", RData.location=RData.location)
DD_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 6 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Frizzled cysteine-rich domain	20	10	31.30	0.0e+00	3.0e-20	SFRP4, FZD1, FZD4, FZD6, FZD7, FZD8, FZD10, FZD3, FZD5, FZD2
Thioredoxin-like	127	10	11.80	3.0e-11	1.5e-10	TXNL1, TXNRD3, PRDX2, NME8, PRDX3, TXNDC2, PRDX5, TXNDC8, TXN2, PRDX4
Galactose-binding domain-like	75	3	4.30	5.4e-04	1.8e-03	EPHA8, MFGE8, TXNL1
Growth factor receptor domain	127	3	2.98	3.8e-03	9.4e-03	RSPO4, MEGF6, RSPO2
Cysteine proteinases	149	3	2.62	6.6e-03	1.3e-02	USP5, USP29, USP36
KRAB domain (Kruppel-associated box)	356	4	1.68	3.3e-02	5.5e-02	ZIM3, ZNF543, ZNF460, ZNF304

• GO molecular function

eTerm <- xPrioritiserPathways(pNode=pDD, priority.top=100, ontology="GOMF", RData.location=RData.location)
DD_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
frizzled binding	37	20	42.00	0.0e+00	0.0e+00	WNT5A, WNT3A, WNT3, WNT7A, WNT5B, FZD1, FZD7, WNT4, WNT11, WNT2, WNT8A, WNT10B, WNT6, WNT7B, WNT8B, WNT10A, WNT2B, WNT9A, WNT9B, WNT16
Wnt-activated receptor activity	21	10	27.80	7.0e-20	8.2e-19	FZD4, FZD1, FZD8, SFRP4, FZD5, FZD2, FZD6, FZD7, FZD3, FZD10
Wnt-protein binding	28	10	24.00	4.0e-18	3.2e-17	FZD4, FZD1, FZD8, SFRP4, FZD5, FZD2, FZD6, FZD7, FZD3, FZD10
receptor agonist activity	15	8	26.40	3.4e-17	2.0e-16	WNT5A, WNT3A, WNT3, WNT7A, WNT4, WNT2, WNT8A, WNT10B
protein disulfide oxidoreductase activity	23	5	13.10	3.8e-09	1.8e-08	TXNRD3, TXN2, TXNDC2, TXNDC8, TXNL1
PDZ domain binding	86	6	7.65	8.4e-07	3.4e-06	FZD4, FZD1, FZD8, FZD2, FZD7, FZD3
microtubule motor activity	68	3	4.06	7.5e-04	2.6e-03	DNAH11, DNAH5, DNAI2
G-protein coupled receptor activity	643	10	3.18	1.6e-03	4.9e-03	FZD4, FZD1, FZD8, SFRP4, FZD5, FZD2, FZD6, FZD7, FZD3, FZD10
ubiquitin-specific protease activity	90	3	3.35	2.1e-03	5.7e-03	USP29, USP5, USP36
cysteine-type endopeptidase activity	94	3	3.25	2.5e-03	6.0e-03	USP29, USP5, USP36

5.4.3 Network-level prioritisation

# find maximum-scoring gene network with the desired node number=20
DD_net <- xPrioritiserSubnet(pNode=pDD, priority.quantite=0.1, subnet.size=21, RData.location=RData.location)

The identified gene network has nodes/genes colored according to their priority scores (see below). Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.

g <- DD_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

• Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Basal cell carcinoma	55	11	30.10	0.0e+00	1.0e-20	WNT2, WNT6, WNT7B, WNT10B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, FZD10
Melanogenesis	101	12	24.10	1.0e-20	7.0e-20	CREBBP, WNT2, WNT6, WNT7B, WNT10B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, FZD10
Wnt signaling pathway	151	13	21.20	1.0e-20	1.0e-19	CREBBP, WNT2, WNT6, WNT7B, WNT10B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, FZD10, SFRP4
Class B/2 (Secretin family receptors)	87	11	23.80	1.1e-19	7.1e-19	WNT2, WNT6, WNT7B, WNT10B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, FZD10
Genes related to Wnt-mediated signal transduction	89	10	21.30	2.0e-17	1.0e-16	WNT2, WNT6, WNT7B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, SFRP4
Wnt signaling network	28	7	26.80	7.1e-16	3.0e-15	WNT2, WNT7B, FZD4, FZD6, FZD7, FZD8, FZD10
Pathways in cancer	325	12	13.00	3.1e-14	1.1e-13	CREBBP, WNT2, WNT6, WNT7B, WNT10B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, FZD10
GPCR ligand binding	400	11	10.50	1.4e-11	4.4e-11	WNT2, WNT6, WNT7B, WNT10B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, FZD10
Hedgehog signaling pathway	56	6	16.10	3.2e-11	8.9e-11	WNT2, WNT6, WNT7B, WNT10B, WNT11, WNT10A
Signaling by GPCR	906	11	6.34	1.7e-07	4.2e-07	WNT2, WNT6, WNT7B, WNT10B, WNT11, FZD4, FZD6, FZD7, FZD8, WNT10A, FZD10

• Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 3 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Cell surface	467	5	3.78	0.0005	0.002	SFRP4, FZD10, WNT6, FZD4, RSPO2
G protein coupled receptor	855	5	2.19	0.0110	0.023	FZD10, FZD4, FZD6, FZD7, FZD8
Tumor suppressor	716	4	1.83	0.0230	0.031	UBA52, AURKC, PLK1, WNT10B

6 Ontology annotations & TPN nominations

In this section, we first describe annotation predictors using ontologies and target genes nominated by ULTRA-DD TPN members, and then integrate them as an additional support for disease-specific genetic prioritisation by Pi.

6.1 Annotation predictors

Using ontologies to annotate genes is one of the most effective ways to reuse existing knowledge. Also it is a scalable way to capture a particular knowledge sphere in an systematic way. An ontology is like a dictionary, containing vocabularies (called terms) and their relations. These terms and relations are understandable by humans, and readable by computers. According to how to organise relationships between terms, there are types of ontologies:

1. structured ontology: terms are organised in a tree-like structure, such as Gene Ontology (Ashburner et al. 2000), Disease Ontology (Schriml et al. 2012), and Phenotype Ontologies in human and mouse (Köhler et al. 2013; C. L. Smith and Eppig 2009);
2. non-structured ontology: terms are not organised as tree but simply listed as keywords, such as a collection of pathways, and gene druggable categories.

When integrating ontologies of different knowledge spheres, the use is limited by the heterogeneous nature: different terms/keywords represent the same or similar knowledge. For this reason, they are better used in a broader context to capture a general category of knowledge, eg a group of diseases instead of a specific disease. We consider the context broadly relevant to immune-related diseases. We choose 5 annotation predictors as informative carriers generalising their relatedness to immunity/inflammation diseases. For a given gene, we look at:

1. OMIM evidence: whether it was identified in OMIM as an immune disease ontology gene;
2. Phenotype evidence: whether it is annotated to immune-related phenotypes from Human Phenotype Ontology (HPO) and/or Mouse Phenotype Ontology (MPO);
3. Function evidence: whether it is annotated to immune/inflammatory terms from Gene Ontology;
4. Pathway evidence: whether it is annotated to immune-related pathways (from KEGG, BioCarta and Reactome);
5. Druggable evidence: whether it is annotated to gene druggable categories from DGIdb.

Since the above predictors are relatively independent to each other, additive scoring scheme is used to calculate annotation scores per gene.

Import annotation predictors (pre-computed)

iAnno <- read.delim(file.path(path.package("PI"),"iAnno.txt"), stringsAsFactors=F)
# gene/target info
iSymbol <- iAnno[,2]
iGenes <- iAnno[,1:3]
# predictor info (including individual scores)
iPS <- iAnno[,22:26]
# additive predictor score
PS <- apply(iPS, 1, sum)
# combine additive predictor score and individual acores
iPS <- cbind(PS=PS, iPS)

The first 5 rows of the data frame iGenes are:

Target.GeneID	Target.Symbol	Target.Name
1	A1BG	alpha-1-B glycoprotein
2	A2M	alpha-2-macroglobulin
3	A2MP1	alpha-2-macroglobulin pseudogene 1
9	NAT1	N-acetyltransferase 1 (arylamine N-acetyltransferase)
10	NAT2	N-acetyltransferase 2 (arylamine N-acetyltransferase)

Their predictor info is stored in the data frame iPS, with the column PS for additive predictor scores:

PS	OMIM	Phenotype	Function	Pathway	Druggable
0	0	0	0	0	0
1	0	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
1	1	0	0	0	0

Distributions of annotation predictor scores

df <- iPS
p <- ggplot(df, aes(PS))
p + geom_bar(fill="deepskyblue") + scale_y_log10() + geom_text(stat="bin",binwidth=1,aes(label=..count..),vjust=0,color="blue") + ylab("Number of genes (predicted)") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + scale_x_continuous(breaks=0:max(df$PS)) + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue"))

Pairwise correlations between predictors

df <- df[df$PS>0, -1]
res <- supraHex::sDistance(t(df), metric="pearson")
diag(res) <- 1
cellnote <- signif(1-res, digits=2)
diag(cellnote) <- NA
visHeatmapAdv(1-res, Rowv=F, Colv=F, colormap="bwr", zlim=c(-1,1), key=F, cexRow=1, cexCol=1, cellnote=cellnote, notecex=0.8, notecol="black", lhei=c(1,5), lwid=c(1,5))

6.2 TPN nominations

# TPN nomination info
iTPN <- iAnno[,9:15]
# flag whether nominated by ULTRA-DD TPN members
iNominated <- iAnno[,9]
# info provided by SGC Oxford
iSGC <- iAnno[,4:8]
# iSGC restricted to nominated targets
df <- iSGC[iNominated>0, ]
df$SGC_PI <- !(df$SGC_PI %in% c("Unassigned","Unassigned - TO",""))

Gene family analysis

Below show results of family analysis (provided by Brian and David).

The top family is integral membrane proteins (IMP), followed by histone proteins (histone lysine demethylase; KDM) and ubiquitin-related proteins. Other top families include DENN domain proteins.

Allocations to SGC PIs

As seen below, 714 genes with 490 (68%) have been allocation to specific SGC-Oxford PIs.

SGC PI allocations are conditioned on SG/ChemBio Tractability:

Generally speaking, targets with good tractability are more likely to be allocated to SGC PIs.

Distributions of annotation predictor scores for nominated genes

Distributions of annotation predictor scores are conditioned on whether genes are nominated or not:

It can be seen that nominated genes tend to receive a higher annotation scores than non-nominated genes do.

6.3 Integration for Ankylosing Spondylitis

Recall: AS genes prioritised by Pi are stored in the data frame AS_genes, and the data frame iPS for annotation predictor scores for all genes stored in iSymbol or iGenes (see above).

# find genes with annotation predictors  
ind <- match(rownames(AS_genes), iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine AS_genes iPS iGenes iNominated
AS_combined <- cbind(AS_genes[,c(1,4)], genes_ap)

The top 20 AS-specific genes along with annotation predictors and TPN nominations are listed below:

name	priority	PS	OMIM	Phenotype	Function	Pathway	Druggable	Nominated
CAST	0.05574	1	0	0	0	0	1	0
ERAP2	0.03338	0	0	0	0	0	0	0
ERAP1	0.03171	2	0	0	1	1	0	0
B3GNT2	0.01838	0	0	0	0	0	0	0
LNPEP	0.01767	1	0	0	0	1	0	0
IL23R	0.01765	3	1	1	1	0	0	0
DDX39B	0.01323	1	0	0	0	0	1	0
TBKBP1	0.01230	0	0	0	0	0	0	0
NFKBIL1	0.01154	1	1	0	0	0	0	0
ETS2	0.01151	0	0	0	0	0	0	0
IL12RB2	0.01136	1	0	1	0	0	0	0
KIF21B	0.00948	0	0	0	0	0	0	0
PSMG1	0.00945	0	0	0	0	0	0	0
C1orf106	0.00827	0	0	0	0	0	0	0
IL6R	0.00778	5	1	1	1	1	1	0
TNFRSF1A	0.00690	4	1	1	1	0	1	1
GPR65	0.00683	1	0	0	1	0	0	1
NPEPPS	0.00648	2	0	0	0	1	1	0
KPNB1	0.00645	1	0	0	0	1	0	0
NKX2-3	0.00622	0	0	0	0	0	0	0

Comparing genetic priority and annotation prediction

This violin plot displays density (the violin shape) of genes receiving AS-specific genetic priority versus annotation predictor scores (general indicator of immune-relatedness).

hline <- quantile(AS_combined$priority,0.95) # genetic priority at the top 5%
hline1 <- quantile(AS_combined$priority,1) # genetic priority at the top
p <- ggplot(AS_combined, aes(factor(PS), priority))
p + geom_violin(trim=T,adjust=0.5) + scale_y_log10() + ylab("AS-specific genetic priority") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=hline,color="darkgreen",linetype="dotdash") + geom_vline(xintercept=c(2,5)+0.5,color="transparent",linetype="dotdash") + geom_segment(aes(x=2.5,xend=2.5,y=hline,yend=hline1),color="red",linetype="dotdash") + geom_segment(aes(x=4.5,xend=4.5,y=hline,yend=hline1),color="red",linetype="dotdash")

A horizontal line is for genetic priority at the top 5%, and two vertical lines separate well-annotated genes, moderately annotated genes, and genes seldom annotated. Therefore genes can be divided into 4 areas:

• Top-right: 39 easy targets/genes receiving high genetic priority and being well-annotated. They are as expected considering sufficient existing knowledge in support. The followup risk is low but the novelty is compromised.
• Top-middle: 117 a2maid targets/genes receiving high genetic priority and being moderately annotated. They might represent attractive targets as both the risk and novelty are at the twilight zone. The origin of the wording a2maid is explained here.¹
• Top-left: 599 novel targets/genes receiving high genetic priority but with seldom annotations. Its great novelty comes the high risk to follow-up.
• Bottom: targets unlikely related to AS (maybe other immune diseases).

The 39 easy targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
IL6R	interleukin 6 receptor	0.00778	5	0
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	0.00690	4	1
CARD9	caspase recruitment domain family, member 9	0.00593	4	0
TYK2	tyrosine kinase 2	0.00432	4	0
IFIH1	interferon induced with helicase C domain 1	0.00342	4	0
CD27	CD27 molecule	0.00315	4	0
FCGR3A	Fc fragment of IgG, low affinity IIIa, receptor (CD16a)	0.00261	4	0
IL12B	interleukin 12B	0.00245	5	0
NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	0.00244	4	0
IL2RA	interleukin 2 receptor, alpha	0.00243	5	1
ICAM1	intercellular adhesion molecule 1	0.00173	4	0
NCF4	neutrophil cytosolic factor 4, 40kDa	0.00158	4	0
ADAR	adenosine deaminase, RNA-specific	0.00149	4	1
AIRE	autoimmune regulator	0.00109	4	0
NOTCH1	notch 1	0.00077	4	0
HLA-B	major histocompatibility complex, class I, B	0.00071	4	0
HLA-A	major histocompatibility complex, class I, A	0.00064	5	0
B2M	beta-2-microglobulin	0.00058	5	0
MAPK1	mitogen-activated protein kinase 1	0.00034	4	0
IL12A	interleukin 12A	0.00022	4	0
IFNG	interferon, gamma	0.00021	4	0
STAT4	signal transducer and activator of transcription 4	0.00021	4	0
JAK2	Janus kinase 2	0.00019	5	0
IL1B	interleukin 1, beta	0.00016	4	0
IL1RN	interleukin 1 receptor antagonist	0.00014	5	0
IL2	interleukin 2	0.00014	4	0
IL10	interleukin 10	0.00014	5	0
JAK3	Janus kinase 3	0.00013	4	0
NFKB2	nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100)	0.00013	5	0
STAT3	signal transducer and activator of transcription 3 (acute-phase response factor)	0.00013	4	0
MALT1	MALT1 paracaspase	0.00013	4	0
NFKBIA	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha	0.00012	5	0
PIK3R1	phosphoinositide-3-kinase, regulatory subunit 1 (alpha)	0.00011	5	0
CBL	Cbl proto-oncogene, E3 ubiquitin protein ligase	0.00011	4	0
IL6	interleukin 6	0.00011	5	0
PIK3CA	phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha	0.00010	4	1
CD3E	CD3e molecule, epsilon (CD3-TCR complex)	0.00010	5	0
ITGB2	integrin, beta 2 (complement component 3 receptor 3 and 4 subunit)	0.00009	5	0
BCL10	B-cell CLL/lymphoma 10	0.00009	4	0

The top 10 a2maid targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
ERAP1	endoplasmic reticulum aminopeptidase 1	0.03171	2	0
IL23R	interleukin 23 receptor	0.01765	3	0
NPEPPS	aminopeptidase puromycin sensitive	0.00648	2	0
CACNA1S	calcium channel, voltage-dependent, L type, alpha 1S subunit	0.00579	2	0
FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)	0.00491	2	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	0.00384	2	0
ITGB3	integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)	0.00381	3	0
TBX21	T-box 21	0.00379	2	0
IL1R1	interleukin 1 receptor, type I	0.00375	3	0
NOS2	nitric oxide synthase 2, inducible	0.00372	3	0

The top 5 novel targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
CAST	calpastatin	0.05574	1	0
ERAP2	endoplasmic reticulum aminopeptidase 2	0.03338	0	0
B3GNT2	UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 2	0.01838	0	0
LNPEP	leucyl/cystinyl aminopeptidase	0.01767	1	0
DDX39B	DEAD (Asp-Glu-Ala-Asp) box polypeptide 39B	0.01323	1	0

Label the gene network with annotation prediction

The nodes/genes in AS-specific gene network are color-coded by annotation additive predictor scores.

g <- AS_net
evi <- AS_combined[,3]
names(evi) <- rownames(AS_combined)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Nodes/genes evidenced by an individual predictor and/or nominated by TPN members are highlighted in pink. Here, only nodes with additive predictor scores >=4 are labeled by gene symbols.

g <- AS_net
evi <- AS_combined[, c(4:8,12)]
data <- evi[V(g)$name,]
vertex.label <- V(g)$name
vertex.label[apply(data[,1:5],1,sum)<=3] <- ''
visNetMul(g, data=data, glayout=layout_(g, with_kk()), vertex.label=vertex.label, vertex.shape="sphere", colormap="white-magenta", newpage=F, colorbar=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, vertex.size=8, mtext.cex=0.7,border.color="black",zlim=c(0,1), mtext.side=3, mtext.adj=0)

6.4 Integration for Spondyloarthritis

Recall: SA genes prioritised by Pi are stored in the data frame SA_genes, and the data frame iPS for annotation predictor scores for all genes stored in iSymbol or iGenes (see above).

# find genes with annotation predictors  
ind <- match(rownames(SA_genes), iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine SA_genes iPS iGenes iNominated
SA_combined <- cbind(SA_genes[,c(1,4)], genes_ap)

The top 20 SA-specific genes along with annotation predictors and TPN nominations are listed below:

name	priority	PS	OMIM	Phenotype	Function	Pathway	Druggable	Nominated
DDX39B	0.02378	1	0	0	0	0	1	0
NFKBIL1	0.02074	1	1	0	0	0	0	0
CAST	0.01842	1	0	0	0	0	1	0
ATF6B	0.01621	0	0	0	0	0	0	0
TRAF3IP2	0.01321	1	0	1	0	0	0	0
ERAP2	0.01103	0	0	0	0	0	0	0
ERAP1	0.01048	2	0	0	1	1	0	0
TNIP1	0.00978	0	0	0	0	0	0	0
ANXA6	0.00932	0	0	0	0	0	0	0
LCE3D	0.00912	0	0	0	0	0	0	0
IER3	0.00898	0	0	0	0	0	0	0
LCE3A	0.00858	0	0	0	0	0	0	0
LCE3B	0.00830	0	0	0	0	0	0	0
LCE3C	0.00800	0	0	0	0	0	0	0
TYK2	0.00756	4	1	1	0	1	1	0
LCE3E	0.00749	0	0	0	0	0	0	0
MDC1	0.00737	0	0	0	0	0	0	0
VARS2	0.00673	0	0	0	0	0	0	1
ICAM3	0.00662	2	0	0	1	1	0	0
B3GNT2	0.00607	0	0	0	0	0	0	0

Comparing genetic priority and annotation prediction

This violin plot displays density (the violin shape) of genes receiving SA-specific genetic priority versus annotation predictor scores (general indicator of immune-relatedness).

hline <- quantile(SA_combined$priority,0.95) # genetic priority at the top 5%
hline1 <- quantile(SA_combined$priority,1) # genetic priority at the top
p <- ggplot(SA_combined, aes(factor(PS), priority))
p + geom_violin(trim=T,adjust=0.5) + scale_y_log10() + ylab("Spondyloarthritis-specific genetic priority") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=hline,color="darkgreen",linetype="dotdash") + geom_vline(xintercept=c(2,5)+0.5,color="transparent",linetype="dotdash") + geom_segment(aes(x=2.5,xend=2.5,y=hline,yend=hline1),color="red",linetype="dotdash") + geom_segment(aes(x=4.5,xend=4.5,y=hline,yend=hline1),color="red",linetype="dotdash")

A horizontal line is for genetic priority at the top 5%, and two vertical lines separate well-annotated genes, moderately annotated genes, and genes seldom annotated. Therefore genes can be divided into 4 areas:

• Top-right: 43 easy targets/genes receiving high genetic priority and being well-annotated. They are as expected considering sufficient existing knowledge in support. The followup risk is low but the novelty is compromised.
• Top-middle: 105 a2maid targets/genes receiving high genetic priority and being moderately annotated. They might represent attractive targets as both the risk and novelty are at the twilight zone.
• Top-left: 607 novel targets/genes receiving high genetic priority but with seldom annotations. Its great novelty comes the high risk to follow-up.
• Bottom: targets unlikely related to Spondyloarthritis (maybe other immune diseases).

The 43 easy targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
TYK2	tyrosine kinase 2	0.00756	4	0
IL12B	interleukin 12B	0.00557	5	0
ICAM1	intercellular adhesion molecule 1	0.00410	4	0
HLA-DQA1	major histocompatibility complex, class II, DQ alpha 1	0.00317	4	0
IL6R	interleukin 6 receptor	0.00263	5	0
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	0.00233	4	1
NFKBIA	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha	0.00216	5	0
CARD9	caspase recruitment domain family, member 9	0.00197	4	0
IFIH1	interferon induced with helicase C domain 1	0.00168	4	0
CD27	CD27 molecule	0.00105	4	0
NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	0.00092	4	0
FCGR3A	Fc fragment of IgG, low affinity IIIa, receptor (CD16a)	0.00088	4	0
IL2RA	interleukin 2 receptor, alpha	0.00086	5	1
PSMB8	proteasome (prosome, macropain) subunit, beta type, 8	0.00057	5	0
NCF4	neutrophil cytosolic factor 4, 40kDa	0.00054	4	0
ADAR	adenosine deaminase, RNA-specific	0.00052	4	1
DDX58	DEAD (Asp-Glu-Ala-Asp) box polypeptide 58	0.00052	4	0
C4A	complement component 4A (Rodgers blood group)	0.00043	4	0
AIRE	autoimmune regulator	0.00040	4	0
CFB	complement factor B	0.00037	5	0
TNF	tumor necrosis factor	0.00036	4	0
MAPK1	mitogen-activated protein kinase 1	0.00035	4	0
NOTCH1	notch 1	0.00027	4	0
HLA-B	major histocompatibility complex, class I, B	0.00027	4	0
HLA-A	major histocompatibility complex, class I, A	0.00025	5	0
B2M	beta-2-microglobulin	0.00024	5	0
IKBKG	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma	0.00023	5	0
PTPN11	protein tyrosine phosphatase, non-receptor type 11	0.00019	4	0
CHUK	conserved helix-loop-helix ubiquitous kinase	0.00017	4	0
IKBKB	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta	0.00015	5	0
HLA-DQB1	major histocompatibility complex, class II, DQ beta 1	0.00014	4	0
IL12A	interleukin 12A	0.00014	4	0
NFKB2	nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100)	0.00014	5	0
CD40	CD40 molecule, TNF receptor superfamily member 5	0.00013	5	0
STAT4	signal transducer and activator of transcription 4	0.00013	4	0
ITGB2	integrin, beta 2 (complement component 3 receptor 3 and 4 subunit)	0.00013	5	0
JAK2	Janus kinase 2	0.00012	5	0
IFNG	interferon, gamma	0.00012	4	0
MALT1	MALT1 paracaspase	0.00011	4	0
IL2	interleukin 2	0.00011	4	0
IL1B	interleukin 1, beta	0.00011	4	0
HLA-DRB1	major histocompatibility complex, class II, DR beta 1	0.00010	4	0
STAT3	signal transducer and activator of transcription 3 (acute-phase response factor)	0.00010	4	0

The top 10 a2maid targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
ERAP1	endoplasmic reticulum aminopeptidase 1	0.01048	2	0
ICAM3	intercellular adhesion molecule 3	0.00662	2	0
IL23R	interleukin 23 receptor	0.00590	3	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	0.00545	2	0
TNFAIP3	tumor necrosis factor, alpha-induced protein 3	0.00543	2	1
FYN	FYN proto-oncogene, Src family tyrosine kinase	0.00477	3	0
HLA-E	major histocompatibility complex, class I, E	0.00416	2	0
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	0.00252	3	1
NPEPPS	aminopeptidase puromycin sensitive	0.00215	2	0
CACNA1S	calcium channel, voltage-dependent, L type, alpha 1S subunit	0.00192	2	0

The top 5 novel targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
DDX39B	DEAD (Asp-Glu-Ala-Asp) box polypeptide 39B	0.02378	1	0
NFKBIL1	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor-like 1	0.02074	1	0
CAST	calpastatin	0.01842	1	0
ATF6B	activating transcription factor 6 beta	0.01621	0	0
TRAF3IP2	TRAF3 interacting protein 2	0.01321	1	0

Label the gene network with annotation prediction

The nodes/genes in Spondyloarthritis-specific gene network are color-coded by annotation additive predictor scores.

g <- SA_net
evi <- SA_combined[,3]
names(evi) <- rownames(SA_combined)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Nodes/genes evidenced by an individual predictor and/or nominated by TPN members are highlighted in pink. Here, only nodes with additive predictor scores >=4 are labeled by gene symbols.

g <- SA_net
evi <- SA_combined[, c(4:8,12)]
data <- evi[V(g)$name,]
vertex.label <- V(g)$name
vertex.label[apply(data[,1:5],1,sum)<=3] <- ''
visNetMul(g, data=data, glayout=layout_(g, with_kk()), vertex.label=vertex.label, vertex.shape="sphere", colormap="white-magenta", newpage=F, colorbar=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, vertex.size=8, mtext.cex=0.7,border.color="black",zlim=c(0,1), mtext.side=3, mtext.adj=0)

6.5 Integration for Systemic Lupus Erythematosus

Recall: SLE genes prioritised by Pi are stored in the data frame SLE_genes, and the data frame iPS for annotation predictor scores for all genes stored in iSymbol or iGenes (see above).

# find genes with annotation predictors  
ind <- match(rownames(SLE_genes), iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine SLE_genes iPS iGenes iNominated
SLE_combined <- cbind(SLE_genes[,c(1,4)], genes_ap)

The top 20 SLE-specific genes along with annotation predictors and TPN nominations are listed below:

name	priority	PS	OMIM	Phenotype	Function	Pathway	Druggable	Nominated
ATF6B	0.03584	0	0	0	0	0	0	0
NELFE	0.02596	0	0	0	0	0	0	0
VARS2	0.02149	0	0	0	0	0	0	1
ITGAM	0.02123	3	1	1	1	0	0	1
PBX2	0.01948	0	0	0	0	0	0	0
IRF5	0.01938	3	1	1	0	1	0	1
STAT4	0.01930	4	1	1	0	1	1	0
TNPO3	0.01708	1	1	0	0	0	0	0
CUEDC2	0.01616	0	0	0	0	0	0	0
HLA-DPB1	0.01481	2	1	0	0	1	0	0
RNF114	0.01319	0	0	0	0	0	0	0
CLIC1	0.01301	0	0	0	0	0	0	0
PSMB9	0.01287	3	0	0	1	1	1	0
C2	0.01216	2	0	0	1	1	0	0
NFKBIL1	0.01211	1	1	0	0	0	0	0
PYCARD	0.01192	2	0	0	1	1	0	0
LTA	0.01068	3	1	0	0	1	1	0
SKIV2L	0.01046	0	0	0	0	0	0	0
HNRNPM	0.00989	0	0	0	0	0	0	0
ITGAX	0.00955	0	0	0	0	0	0	0

Comparing genetic priority and annotation prediction

This violin plot displays density (the violin shape) of genes receiving SLE-specific genetic priority versus annotation predictor scores (general indicator of immune-relatedness).

hline <- quantile(SLE_combined$priority,0.95) # genetic priority at the top 5%
hline1 <- quantile(SLE_combined$priority,1) # genetic priority at the top
p <- ggplot(SLE_combined, aes(factor(PS), priority))
p + geom_violin(trim=T,adjust=0.5) + scale_y_log10() + ylab("SLE-specific genetic priority") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=hline,color="darkgreen",linetype="dotdash") + geom_vline(xintercept=c(2,5)+0.5,color="transparent",linetype="dotdash") + geom_segment(aes(x=2.5,xend=2.5,y=hline,yend=hline1),color="red",linetype="dotdash") + geom_segment(aes(x=4.5,xend=4.5,y=hline,yend=hline1),color="red",linetype="dotdash")

A horizontal line is for genetic priority at the top 5%, and two vertical lines separate well-annotated genes, moderately annotated genes, and genes seldom annotated. Therefore genes can be divided into 4 areas:

• Top-right: 44 easy targets/genes receiving high genetic priority and being well-annotated. They are as expected considering sufficient existing knowledge in support. The followup risk is low but the novelty is compromised.
• Top-middle: 104 a2maid targets/genes receiving high genetic priority and being moderately annotated. They might represent attractive targets as both the risk and novelty are at the twilight zone.
• Top-left: 607 novel targets/genes receiving high genetic priority but with seldom annotations. Its great novelty comes the high risk to follow-up.
• Bottom: targets unlikely related to SLE (maybe other immune diseases).

The 44 easy targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
STAT4	signal transducer and activator of transcription 4	0.01930	4	0
C4A	complement component 4A (Rodgers blood group)	0.00630	4	0
HLA-DQA1	major histocompatibility complex, class II, DQ alpha 1	0.00622	4	0
CFB	complement factor B	0.00517	5	0
PTPN22	protein tyrosine phosphatase, non-receptor type 22 (lymphoid)	0.00406	4	2
TYK2	tyrosine kinase 2	0.00226	4	0
IRF8	interferon regulatory factor 8	0.00189	4	0
IFIH1	interferon induced with helicase C domain 1	0.00160	4	0
IRF7	interferon regulatory factor 7	0.00155	4	0
CDKN1B	cyclin-dependent kinase inhibitor 1B (p27, Kip1)	0.00134	4	0
HRAS	Harvey rat sarcoma viral oncogene homolog	0.00111	4	0
ICAM1	intercellular adhesion molecule 1	0.00100	4	0
IL10	interleukin 10	0.00097	5	0
TNF	tumor necrosis factor	0.00097	4	0
FCGR3A	Fc fragment of IgG, low affinity IIIa, receptor (CD16a)	0.00091	4	0
PNP	purine nucleoside phosphorylase	0.00090	4	0
IL12A	interleukin 12A	0.00079	4	0
MAPK1	mitogen-activated protein kinase 1	0.00074	4	0
CIITA	class II, major histocompatibility complex, transactivator	0.00048	4	1
HLA-DQB1	major histocompatibility complex, class II, DQ beta 1	0.00034	4	0
CHUK	conserved helix-loop-helix ubiquitous kinase	0.00027	4	0
MASP2	mannan-binding lectin serine peptidase 2	0.00023	5	0
HLA-DRB1	major histocompatibility complex, class II, DR beta 1	0.00023	4	0
BCL2	B-cell CLL/lymphoma 2	0.00020	5	0
C1QA	complement component 1, q subcomponent, A chain	0.00019	4	0
CARD9	caspase recruitment domain family, member 9	0.00018	4	0
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	0.00017	4	1
IKBKG	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma	0.00016	5	0
MYD88	myeloid differentiation primary response 88	0.00016	4	0
IL12B	interleukin 12B	0.00016	5	0
NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	0.00016	4	0
PDGFRA	platelet-derived growth factor receptor, alpha polypeptide	0.00015	4	0
ITGB2	integrin, beta 2 (complement component 3 receptor 3 and 4 subunit)	0.00014	5	0
C3	complement component 3	0.00014	5	0
IL2RA	interleukin 2 receptor, alpha	0.00013	5	1
PSTPIP1	proline-serine-threonine phosphatase interacting protein 1	0.00013	4	0
MEFV	Mediterranean fever	0.00013	4	0
CREBBP	CREB binding protein	0.00012	4	1
IL1B	interleukin 1, beta	0.00012	4	0
TLR2	toll-like receptor 2	0.00012	4	0
IFNG	interferon, gamma	0.00012	4	0
ITK	IL2-inducible T-cell kinase	0.00012	5	0
NLRP3	NLR family, pyrin domain containing 3	0.00011	4	1
CD40	CD40 molecule, TNF receptor superfamily member 5	0.00011	5	0

The top 10 a2maid targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	0.02123	3	1
IRF5	interferon regulatory factor 5	0.01938	3	1
HLA-DPB1	major histocompatibility complex, class II, DP beta 1	0.01481	2	0
PSMB9	proteasome (prosome, macropain) subunit, beta type, 9	0.01287	3	0
C2	complement component 2	0.01216	2	0
PYCARD	PYD and CARD domain containing	0.01192	2	0
LTA	lymphotoxin alpha	0.01068	3	0
STAT1	signal transducer and activator of transcription 1, 91kDa	0.00800	3	0
TNFAIP3	tumor necrosis factor, alpha-induced protein 3	0.00392	2	1
TNFSF4	tumor necrosis factor (ligand) superfamily, member 4	0.00374	2	0

The top 5 novel targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
ATF6B	activating transcription factor 6 beta	0.03584	0	0
NELFE	negative elongation factor complex member E	0.02596	0	0
VARS2	valyl-tRNA synthetase 2, mitochondrial	0.02149	0	1
PBX2	pre-B-cell leukemia homeobox 2	0.01948	0	0
TNPO3	transportin 3	0.01708	1	0

Label the gene network with annotation prediction

The nodes/genes in SLE-specific gene network are color-coded by annotation additive predictor scores.

g <- SLE_net
evi <- SLE_combined[,3]
names(evi) <- rownames(SLE_combined)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Nodes/genes evidenced by an individual predictor and/or nominated by TPN members are highlighted in pink. Here, only nodes with additive predictor scores >=4 are labeled by gene symbols.

g <- SLE_net
evi <- SLE_combined[, c(4:8,12)]
data <- evi[V(g)$name,]
vertex.label <- V(g)$name
vertex.label[apply(data[,1:5],1,sum)<=3] <- ''
visNetMul(g, data=data, glayout=layout_(g, with_kk()), vertex.label=vertex.label, vertex.shape="sphere", colormap="white-magenta", newpage=F, colorbar=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, vertex.size=8, mtext.cex=0.7,border.color="black",zlim=c(0,1), mtext.side=3, mtext.adj=0)

7 Cross-disease comparisons

7.1 Gene-level comparisons

Recall: disease-centric genetic prioritisation for genes by Pi are stored respectively in three data frames, that is, AS_genes for Ankylosing Spondylitis, SA_genes for Spondyloarthritis and SLE_genes for Systemic Lupus Erythematosus as exemplars. The data frame iPS stores annotation predictor scores for all genes, the vector iNominated for nominated genes, and iSymbol or iGenes for gene info.

# Ankylosing Spondylitis (AS)
ind <- match(iSymbol, AS_genes$name)
df_AS <- AS_genes[ind,]
# Spondyloarthritis (SA)
ind <- match(iSymbol, SA_genes$name)
df_SA <- SA_genes[ind,]
# Systemic Lupus Erythematosus (SLE)
ind <- match(iSymbol, SLE_genes$name)
df_SLE <- SLE_genes[ind,]

# Combine into a data frame 'df_rank' and `df_priority`
## rank
df_rank <- cbind(AS=df_AS$rank, SA=df_SA$rank, SLE=df_SLE$rank)
## priority
df_priority <- cbind(AS=df_AS$priority, SA=df_SA$priority, SLE=df_SLE$priority)
rownames(df_rank) <- rownames(df_priority) <- iSymbol

Venn diagram of the top 200 genes across diseases

ntop <- 200
data <- list()
data$AS <- iSymbol[which(df_rank[,1] <= ntop)]
data$SA <- iSymbol[which(df_rank[,2] <= ntop)]
data$SLE <- iSymbol[which(df_rank[,3] <= ntop)]
category.names <- c('Ankylosing Spondylitis (AS)', 'Spondyloarthritis (SA)', 'Systemic Lupus Erythematosus (SLE)')
vp <- venn.diagram(x=data, filename=NULL, fill=c("skyblue","pink1","mediumorchid"), category.names=names(data), margin=0.15)
grid.draw(vp)

The 14 genes common to three diseases (along with annotation additive predictor scores and how many times being nominated by TPN members) are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
UBE2L3	ubiquitin-conjugating enzyme E2L 3	1	1
ICAM1	intercellular adhesion molecule 1	4	0
IFIH1	interferon induced with helicase C domain 1	4	0
TYK2	tyrosine kinase 2	4	0
FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)	2	0
ICAM3	intercellular adhesion molecule 3	2	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	2	0
DDX39B	DEAD (Asp-Glu-Ala-Asp) box polypeptide 39B	1	0
NFKBIL1	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor-like 1	1	0
CDC37	cell division cycle 37	0	0
FDX1L	ferredoxin 1-like	0	0
RAVER1	ribonucleoprotein, PTB-binding 1	0	0
RLTPR	RGD motif, leucine rich repeats, tropomodulin domain and proline-rich containing	0	0
ZGLP1	zinc finger, GATA-like protein 1	0	0

Venn diagram of the top 200 genes in any diseases vs 714 TPN nominated genes

ntop <- 200
data <- list()
data$AS <- iSymbol[which(df_rank[,1] <= ntop)]
data$SA <- iSymbol[which(df_rank[,2] <= ntop)]
data$SLE <- iSymbol[which(df_rank[,3] <= ntop)]
data$Prioritised <- base::Reduce(union, data[1:3])
data$Nominated <- iSymbol[which(iNominated>0)]
category.names <- c('Prioritised', 'Nominated')
vp <- venn.diagram(x=data[4:5], filename=NULL, fill=c("red","orange"), category.names=category.names, margin=0.15, cat.pos=1)
grid.draw(vp)

The 30 genes common to disease-specific genetic prioritisations and TPN nominations (along with annotation additive predictor scores and how many times being nominated by TPN members) are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
PXK	PX domain containing serine/threonine kinase	1	4
PTPN22	protein tyrosine phosphatase, non-receptor type 22 (lymphoid)	4	2
IL2RA	interleukin 2 receptor, alpha	5	1
ADAR	adenosine deaminase, RNA-specific	4	1
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	4	1
BLK	BLK proto-oncogene, Src family tyrosine kinase	3	1
IRF5	interferon regulatory factor 5	3	1
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	3	1
TNFAIP3	tumor necrosis factor, alpha-induced protein 3	2	1
BANK1	B-cell scaffold protein with ankyrin repeats 1	1	1
GPR65	G protein-coupled receptor 65	1	1
IL23A	interleukin 23, alpha subunit p19	1	1
KAT8	K(lysine) acetyltransferase 8	1	1
SCNN1A	sodium channel, non voltage gated 1 alpha subunit	1	1
SLC11A1	solute carrier family 11 (proton-coupled divalent metal ion transporter), member 1	1	1
SLC15A4	solute carrier family 15 (oligopeptide transporter), member 4	1	1
SLC5A2	solute carrier family 5 (sodium/glucose cotransporter), member 2	1	1
SPHK2	sphingosine kinase 2	1	1
UBE2L3	ubiquitin-conjugating enzyme E2L 3	1	1
BRWD1	bromodomain and WD repeat domain containing 1	0	1
CCDC101	coiled-coil domain containing 101	0	1
MBTPS2	membrane-bound transcription factor peptidase, site 2	0	1
ORAI3	ORAI calcium release-activated calcium modulator 3	0	1
PARP11	poly (ADP-ribose) polymerase family, member 11	0	1
RAB5A	RAB5A, member RAS oncogene family	0	1
RIOK2	RIO kinase 2	0	1
SLC9A8	solute carrier family 9, subfamily A (NHE8, cation proton antiporter 8), member 8	0	1
SMPD2	sphingomyelin phosphodiesterase 2, neutral membrane (neutral sphingomyelinase)	0	1
TET3	tet methylcytosine dioxygenase 3	0	1
VARS2	valyl-tRNA synthetase 2, mitochondrial	0	1

7.2 Pathway-level comparisons

Recall: disease-centric genetic prioritisation for pathways by Pi are stored respectively in three data frames, that is, AS_pathways for Ankylosing Spondylitis, SA_pathways for Spondyloarthritis and SLE_pathways for Systemic Lupus Erythematosus as exemplars.

Load all pathways into a data frame iPathway:

org.Hs.egMsigdbC2CPall <- xRDataLoader(RData.customised='org.Hs.egMsigdbC2CPall', RData.location=RData.location)
iPathway <- org.Hs.egMsigdbC2CPall$set_info[, c(1,2)]

# Ankylosing Spondylitis (AS)
ind <- match(rownames(iPathway), rownames(AS_pathways))
df_AS <- AS_pathways[ind,]
# Spondyloarthritis (SA)
ind <- match(rownames(iPathway), rownames(SA_pathways))
df_SA <- SA_pathways[ind,]
# Systemic Lupus Erythematosus (SLE)
ind <- match(rownames(iPathway), rownames(SLE_pathways))
df_SLE <- SLE_pathways[ind,]

# Combine into a data frame 'df_adjp'
## adjusted p-values
df_adjp <- cbind(AS=df_AS$adjp, SA=df_SA$adjp, SLE=df_SLE$adjp)
rownames(df_adjp) <- iPathway$name

This barplot displays prioritised pathways for three diseases, in which horizontal lines are used to indicate three groups of pathways prioritised:

• Top panel: pathways common to all diseases, including IL23-mediated signaling events, IL27-mediated signaling events.
• Middle panel: pathways common to any two diseases.
• Bottom panel: pathways unique to individual diseases.

7.3 Network-level comparisons

Recall: the gene network identified by Pi are stored respectively in three data frames, that is, AS_net for Ankylosing Spondylitis, SA_net for Spondyloarthritis and SLE_net for Systemic Lupus Erythematosus as exemplars. The data frame iPS stores annotation predictor scores for all genes, the vector iNominated for nominated genes, and iSymbol or iGenes for gene info.

# Ankylosing Spondylitis (AS)
ind <- match(iSymbol, V(AS_net)$name)
df_AS <- V(AS_net)$name[ind]
# Spondyloarthritis (SA)
ind <- match(iSymbol, V(SA_net)$name)
df_SA <- V(SA_net)$name[ind]
# Systemic Lupus Erythematosus (SLE)
ind <- match(iSymbol, V(SLE_net)$name)
df_SLE <- V(SLE_net)$name[ind]

# Combine into a data frame 'df_net'
df_net <- cbind(AS=df_AS, SA=df_SA, SLE=df_SLE)
rownames(df_net) <- iSymbol

Venn diagram of network genes across diseases

data <- list()
data$AS <- iSymbol[!is.na(df_net[,1])]
data$SA <- iSymbol[!is.na(df_net[,2])]
data$SLE <- iSymbol[!is.na(df_net[,3])]
category.names <- c('Ankylosing Spondylitis (AS)', 'Spondyloarthritis (SA)', 'Systemic Lupus Erythematosus (SLE)')
vp <- venn.diagram(x=data, filename=NULL, fill=c("skyblue","pink1","mediumorchid"), category.names=names(data), margin=0.2)
grid.draw(vp)

The common part of the gene network shared by AS, SA and SLE

# identify common parts of SLE-network and AS-network. 
net_AS_SA_SLE <- graph.intersection(AS_net, SA_net, SLE_net, keep.all.vertices=F)

# append genes with info about annotation predictors  
g <- net_AS_SA_SLE
ind <- match(V(g)$name, iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine iGenes iPS iNominated
g_info <- genes_ap[,c(8:9,1,10)]
colnames(g_info)[3] <- 'Annotated'
rownames(g_info) <- g_info[,1]

The 6 genes common to three diseases (along with annotation additive predictor scores and how many times being nominated by TPN members) are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
ICAM1	intercellular adhesion molecule 1	4	0
TYK2	tyrosine kinase 2	4	0
ICAM3	intercellular adhesion molecule 3	2	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	2	0
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2	0
CDC37	cell division cycle 37	0	0

The network nodes/genes shared by three diseases are color-coded by annotation additive predictor scores.

# visualise intersected network part with nodes/genes color-encoded by annotation predictors
evi <- g_info[,3]
names(evi) <- rownames(g_info)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

The common part of the gene network shared by AS and SA

# identify common parts of AS-network and SA-network. 
net_AS_SA <- graph.intersection(AS_net, SA_net, keep.all.vertices=F)

# append genes with info about annotation predictors  
g <- net_AS_SA
ind <- match(V(g)$name, iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine iGenes iPS iNominated
g_info <- genes_ap[,c(8:9,1,10)]
colnames(g_info)[3] <- 'Annotated'
rownames(g_info) <- g_info[,1]

The common 21 genes along with annotation predictors and TPN nominations are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	4	1
SCNN1A	sodium channel, non voltage gated 1 alpha subunit	1	1
IL12B	interleukin 12B	5	0
IL6R	interleukin 6 receptor	5	0
ICAM1	intercellular adhesion molecule 1	4	0
TYK2	tyrosine kinase 2	4	0
IL1R1	interleukin 1 receptor, type I	3	0
IL23R	interleukin 23 receptor	3	0
ITGB3	integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)	3	0
NOS2	nitric oxide synthase 2, inducible	3	0
ERAP1	endoplasmic reticulum aminopeptidase 1	2	0
ICAM3	intercellular adhesion molecule 3	2	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	2	0
NPEPPS	aminopeptidase puromycin sensitive	2	0
TBX21	T-box 21	2	0
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2	0
IL12RB2	interleukin 12 receptor, beta 2	1	0
LNPEP	leucyl/cystinyl aminopeptidase	1	0
CDC37	cell division cycle 37	0	0
MRPL4	mitochondrial ribosomal protein L4	0	0
RUNX3	runt-related transcription factor 3	0	0

The network nodes/genes shared by AS and SA are color-coded by annotation additive predictor scores.

# visualise intersected network part with nodes/genes color-encoded by annotation predictors
evi <- g_info[,3]
names(evi) <- rownames(g_info)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

The common part of the gene network shared by SLE and SA

The common g vcount(g) genes along with annotation predictors and TPN nominations are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	3	1
ICAM1	intercellular adhesion molecule 1	4	0
TYK2	tyrosine kinase 2	4	0
FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)	2	0
ICAM3	intercellular adhesion molecule 3	2	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	2	0
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2	0
SQSTM1	sequestosome 1	1	0
TRIM56	tripartite motif containing 56	1	0
CDC37	cell division cycle 37	0	0
ITGAX	integrin, alpha X (complement component 3 receptor 4 subunit)	0	0
RPL34	ribosomal protein L34	0	0

The network nodes/genes shared by SLE and SA are color-coded by annotation additive predictor scores.

The common part of the gene network shared by SLE and AS

The common 7 genes along with annotation predictors and TPN nominations are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
ICAM1	intercellular adhesion molecule 1	4	0
TYK2	tyrosine kinase 2	4	0
ICAM3	intercellular adhesion molecule 3	2	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	2	0
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2	0
SOCS1	suppressor of cytokine signaling 1	1	0
CDC37	cell division cycle 37	0	0

The network nodes/genes shared by SLE and AS are color-coded by annotation additive predictor scores.

8 Benchmarking

Existing drug targets are stored in DrugBank.

Import drugs and targets

DrugBank <- xRDataLoader(RData.customised='DrugBank', RData.location=RData.location)

8.1 Ankylosing Spondylitis

# find AS approved or experimental drugs and their target genes
ind1 <- grep('ankylosing',DrugBank$indication, ignore.case=T, perl=T)
ind2 <- grep('approved|experimental',DrugBank$groups, ignore.case=T, perl=T)
ind <- intersect(ind1, ind2)
AS_targets <- unique(DrugBank[ind, c("GeneID","Symbol","description")])

The 30 AS existing drug target genes are:

GeneID	Symbol	description
231	AKR1B1	aldo-keto reductase family 1, member B1 (aldose reductase)
1645	AKR1C1	aldo-keto reductase family 1, member C1
712	C1QA	complement component 1, q subcomponent, A chain
713	C1QB	complement component 1, q subcomponent, B chain
714	C1QC	complement component 1, q subcomponent, C chain
715	C1R	complement component 1, r subcomponent
716	C1S	complement component 1, s subcomponent
3577	CXCR1	chemokine (C-X-C motif) receptor 1
1909	EDNRA	endothelin receptor type A
2209	FCGR1A	Fc fragment of IgG, high affinity Ia, receptor (CD64)
2212	FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)
2213	FCGR2B	Fc fragment of IgG, low affinity IIb, receptor (CD32)
9103	FCGR2C	Fc fragment of IgG, low affinity IIc, receptor for (CD32) (gene/pseudogene)
3551	IKBKB	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta
4790	NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1
4791	NFKB2	nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100)
4792	NFKBIA	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha
5467	PPARD	peroxisome proliferator-activated receptor delta
5562	PRKAA1	protein kinase, AMP-activated, alpha 1 catalytic subunit
5563	PRKAA2	protein kinase, AMP-activated, alpha 2 catalytic subunit
5564	PRKAB1	protein kinase, AMP-activated, beta 1 non-catalytic subunit
5565	PRKAB2	protein kinase, AMP-activated, beta 2 non-catalytic subunit
5571	PRKAG1	protein kinase, AMP-activated, gamma 1 non-catalytic subunit
51422	PRKAG2	protein kinase, AMP-activated, gamma 2 non-catalytic subunit
53632	PRKAG3	protein kinase, AMP-activated, gamma 3 non-catalytic subunit
11251	PTGDR2	prostaglandin D2 receptor 2
5740	PTGIS	prostaglandin I2 (prostacyclin) synthase
5742	PTGS1	prostaglandin-endoperoxide synthase 1 (prostaglandin G/H synthase and cyclooxygenase)
5743	PTGS2	prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)
6197	RPS6KA3	ribosomal protein S6 kinase, 90kDa, polypeptide 3

8.1.1 Parameter optimisation

9 References

Below is a list of references that this work stands on:

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1. The wording a2maid comes from the mermaid (describing the maid of the sea having a fish’s tail), and instead of the maid of the sea, replacing with a2 turns to be the maid of our lab having two PhD students Anna Sanniti and Alicia Lledo Lara.↩